arXiv Machine Learning
Jun 2
Theoretical Analysis of Engression and Reverse Markov Engression
arXiv:2606. 01002v1 Announce Type: cross Abstract: Engression is a recently proposed and effective framework for conditional distribution learning.
Error Analysis of Neural-Network-Based Engression
arXiv:2607. 27723v1 Announce Type: cross Abstract: Engression (Shen and Meinshausen, 2024) learns a conditional distribution by fitting a generative model $Y = f(X,\varepsilon)$ under the energy score, a strictly proper scoring rule.
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arXiv Machine Learning
Jul 22
Decafs: Disentangled Conditional adversarial Flows
arXiv:2607. 18755v1 Announce Type: new Abstract: Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings.
arXiv AI
Jun 24
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
arXiv:2606. 23920v1 Announce Type: cross Abstract: The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-defined target distributions such as a geometric combination of the source distributions.
Hugging Face Trending Papers
Jul 21
Decafs: Disentangled Conditional adversarial Flows
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation.
arXiv Machine Learning
6d ago
A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
arXiv:2608. 11613v1 Announce Type: new Abstract: In this paper, we propose a local Sinkhorn divergence framework for conditional distribution reconstruction of multidimensional random fields.
arXiv AI
Jun 9
Generation Properties of Stochastic Interpolation under Finite Training Set
arXiv:2509. 21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations.
arXiv Machine Learning
Jul 21
Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations
arXiv:2607. 16725v1 Announce Type: cross Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant.
arXiv AI
Jul 28
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
arXiv:2410. 02596v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms.
Hugging Face Trending Papers
Jul 5
Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the task, data, or input condition.
arXiv AI
Jun 10
A Theory on Flow Matching with Neural Networks
arXiv:2606. 10089v1 Announce Type: cross Abstract: In this work, we develop theoretical foundation for flow matching with neural-network-parameterized conditional velocity fields.
arXiv Machine Learning
Jul 7
Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
arXiv:2607. 04360v1 Announce Type: cross Abstract: Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains.
Hugging Face Trending Papers
Aug 12
A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
In this paper, we propose a local Sinkhorn divergence framework for conditional distribution reconstruction of multidimensional random fields. By utilizing the debiased Sinkhorn divergence, our proposed approach develops a differentiable and computationally efficient local distribution matching objective to train stochastic neural networks (SNNs).